An Integrated Framework for Friend Recommender System based on Graph Theoretic Approach

14 pages•Published: March 9, 2020

Runa Ganguli, Akash Mehta, Narayan Debnath, Sultan Aljahdali and Soumya Sen

Abstract

Study of relationships established in social media is an emerging area of research. Online Social Network (OSN) is a collection of social entities carrying a lot of information that enriches the network. A structured modelling of the OSN dataset is required for informative knowledge mining and efficient Social Network Analysis (SNA). Graphical representation of data helps in analysing the structural properties, study of dense substructure, cluster formation and identifying the numerous types of entities exhibiting associations based on different activity fields. This paper discusses about various ways of graph theoretic representations of OSN including structure-based and content or interaction-based approaches. An integrated framework is proposed in this paper that learns from various user attributes and its associated interactions, network structure, timeline history, etc from a polarized OSN Graph for generating an efficient Friend Suggestion Recommender System.

Keyphrases: cluster, dense substructure, friend suggestion, graph, osn, polarized weight, recommender system, sna, triads

In: Gordon Lee and Ying Jin (editors). Proceedings of 35th International Conference on Computers and Their Applications, vol 69, pages 242-255.

Links:	https://easychair.org/publications/paper/q93p
	https://doi.org/10.29007/4bwn

BibTeX entry

@inproceedings{CATA2020:Integrated_Framework_Friend_Recommender,
  author    = {Runa Ganguli and Akash Mehta and Narayan Debnath and Sultan Aljahdali and Soumya Sen},
  title     = {An Integrated Framework for Friend Recommender System based on Graph Theoretic Approach},
  booktitle = {Proceedings of 35th International Conference on Computers and Their Applications},
  editor    = {Gordon Lee and Ying Jin},
  series    = {EPiC Series in Computing},
  volume    = {69},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/q93p},
  doi       = {10.29007/4bwn},
  pages     = {242-255},
  year      = {2020}}

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